Table REF  shows that PSENet [1]} and PAN [2]} achieve the optimal \(\textit {F-measure}\)  on CTW under the evaluation protocols IOU@0.5 and DetEval-v2 respectively.On the dataset TOT, DB [3]} and PAN [2]} have obtained the best \(\textit {F-measure}\)  under IOU@0.5 and DetEval-v2, respectively.In the terms of the speed, DB [3]} significantly outperforms other methods.Meanwhile, the speed of the component-wise based methods TextSnake [6]} and DRRGN [7]} is distinctly slower than the pixel-wise based methods (, PSENet [1]}, PAN [2]}, DB [3]}, etc.). Some visualized detection results are presented in Figure REF .These quantitative and qualitative experimental results indicate that an older method can be better than a newer method in performance or speed under fair comparisons. It is because the improvements in the original methods mainly come from some tricks (, more training data, stronger backbone, well-designed feature fusion strategies, etc.). To further verify the ability of accurately localizing the text contours, we use stricter thresholds under the evaluation protocol IOU. As show in Figure REF , we observe that PAN [2]} is more robust to the change of IOU threshold than other methods. Similarly, the qualitative results in Figure REF  have also shown that PAN [2]} can achieve more accurate text contours.
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